{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a8df07b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "# pip install scikit-learn\n",
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "091a03ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "hours = np.arange(0.5, 6, 0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1bac76da",
   "metadata": {},
   "outputs": [],
   "source": [
    "outcome = np.array([0,0,0,0,0,0,1,0,0,1,1,1,1,1,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "27ff8e1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame.from_records(zip(hours, outcome), columns=['hours', 'outcome'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "124b3554",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hours</th>\n",
       "      <th>outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3.5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5.5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    hours  outcome\n",
       "0     0.5        0\n",
       "1     1.0        0\n",
       "2     1.5        0\n",
       "3     2.0        0\n",
       "4     2.5        0\n",
       "5     3.0        0\n",
       "6     3.5        1\n",
       "7     4.0        0\n",
       "8     4.5        0\n",
       "9     5.0        1\n",
       "10    5.5        1"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "35322905",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x13f81bbb0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = df['hours']\n",
    "y = df['outcome']\n",
    "plt.scatter(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c1504f99",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 11 entries, 0 to 10\n",
      "Data columns (total 2 columns):\n",
      " #   Column   Non-Null Count  Dtype  \n",
      "---  ------   --------------  -----  \n",
      " 0   hours    11 non-null     float64\n",
      " 1   outcome  11 non-null     int64  \n",
      "dtypes: float64(1), int64(1)\n",
      "memory usage: 304.0 bytes\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "80492877",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='Frequency'>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x.plot.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "58937096",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='Frequency'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y.plot.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e60c7e5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "25e91dff",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Expected 2D array, got 1D array instead:\narray=[0.5 1.  1.5 2.  2.5 3.  3.5 4.  4.5 5.  5.5].\nReshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Input \u001b[0;32mIn [11]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mlr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43my\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.pyenv/versions/3.10.1/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1508\u001b[0m, in \u001b[0;36mLogisticRegression.fit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m   1505\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   1506\u001b[0m     _dtype \u001b[38;5;241m=\u001b[39m [np\u001b[38;5;241m.\u001b[39mfloat64, np\u001b[38;5;241m.\u001b[39mfloat32]\n\u001b[0;32m-> 1508\u001b[0m X, y \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_data\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1509\u001b[0m \u001b[43m    \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1510\u001b[0m \u001b[43m    \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1511\u001b[0m \u001b[43m    \u001b[49m\u001b[43maccept_sparse\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcsr\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1512\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m_dtype\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1513\u001b[0m \u001b[43m    \u001b[49m\u001b[43morder\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mC\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1514\u001b[0m \u001b[43m    \u001b[49m\u001b[43maccept_large_sparse\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msolver\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mliblinear\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43msag\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43msaga\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1515\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1516\u001b[0m check_classification_targets(y)\n\u001b[1;32m   1517\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mclasses_ \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39munique(y)\n",
      "File \u001b[0;32m~/.pyenv/versions/3.10.1/lib/python3.10/site-packages/sklearn/base.py:581\u001b[0m, in \u001b[0;36mBaseEstimator._validate_data\u001b[0;34m(self, X, y, reset, validate_separately, **check_params)\u001b[0m\n\u001b[1;32m    579\u001b[0m         y \u001b[38;5;241m=\u001b[39m check_array(y, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mcheck_y_params)\n\u001b[1;32m    580\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 581\u001b[0m         X, y \u001b[38;5;241m=\u001b[39m \u001b[43mcheck_X_y\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mcheck_params\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    582\u001b[0m     out \u001b[38;5;241m=\u001b[39m X, y\n\u001b[1;32m    584\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m no_val_X \u001b[38;5;129;01mand\u001b[39;00m check_params\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mensure_2d\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mTrue\u001b[39;00m):\n",
      "File \u001b[0;32m~/.pyenv/versions/3.10.1/lib/python3.10/site-packages/sklearn/utils/validation.py:964\u001b[0m, in \u001b[0;36mcheck_X_y\u001b[0;34m(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, estimator)\u001b[0m\n\u001b[1;32m    961\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m y \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    962\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124my cannot be None\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 964\u001b[0m X \u001b[38;5;241m=\u001b[39m \u001b[43mcheck_array\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    965\u001b[0m \u001b[43m    \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    966\u001b[0m \u001b[43m    \u001b[49m\u001b[43maccept_sparse\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maccept_sparse\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    967\u001b[0m \u001b[43m    \u001b[49m\u001b[43maccept_large_sparse\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maccept_large_sparse\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    968\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    969\u001b[0m \u001b[43m    \u001b[49m\u001b[43morder\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43morder\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    970\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    971\u001b[0m \u001b[43m    \u001b[49m\u001b[43mforce_all_finite\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mforce_all_finite\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    972\u001b[0m \u001b[43m    \u001b[49m\u001b[43mensure_2d\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mensure_2d\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    973\u001b[0m \u001b[43m    \u001b[49m\u001b[43mallow_nd\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mallow_nd\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    974\u001b[0m \u001b[43m    \u001b[49m\u001b[43mensure_min_samples\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mensure_min_samples\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    975\u001b[0m \u001b[43m    \u001b[49m\u001b[43mensure_min_features\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mensure_min_features\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    976\u001b[0m \u001b[43m    \u001b[49m\u001b[43mestimator\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mestimator\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    977\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    979\u001b[0m y \u001b[38;5;241m=\u001b[39m _check_y(y, multi_output\u001b[38;5;241m=\u001b[39mmulti_output, y_numeric\u001b[38;5;241m=\u001b[39my_numeric)\n\u001b[1;32m    981\u001b[0m check_consistent_length(X, y)\n",
      "File \u001b[0;32m~/.pyenv/versions/3.10.1/lib/python3.10/site-packages/sklearn/utils/validation.py:769\u001b[0m, in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator)\u001b[0m\n\u001b[1;32m    767\u001b[0m     \u001b[38;5;66;03m# If input is 1D raise error\u001b[39;00m\n\u001b[1;32m    768\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m array\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m--> 769\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m    770\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mExpected 2D array, got 1D array instead:\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124marray=\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    771\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mReshape your data either using array.reshape(-1, 1) if \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    772\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124myour data has a single feature or array.reshape(1, -1) \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    773\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mif it contains a single sample.\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(array)\n\u001b[1;32m    774\u001b[0m         )\n\u001b[1;32m    776\u001b[0m \u001b[38;5;66;03m# make sure we actually converted to numeric:\u001b[39;00m\n\u001b[1;32m    777\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m dtype_numeric \u001b[38;5;129;01mand\u001b[39;00m array\u001b[38;5;241m.\u001b[39mdtype\u001b[38;5;241m.\u001b[39mkind \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mOUSV\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n",
      "\u001b[0;31mValueError\u001b[0m: Expected 2D array, got 1D array instead:\narray=[0.5 1.  1.5 2.  2.5 3.  3.5 4.  4.5 5.  5.5].\nReshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample."
     ]
    }
   ],
   "source": [
    "lr.fit(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "271c6c55",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = x.values.reshape(-1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "114d6d85",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.fit(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1fde05fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6.14262681e-04, 9.99385737e-01]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.predict_proba([[12]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "606bf31b",
   "metadata": {},
   "outputs": [],
   "source": [
    "hours = np.arange(0, 30, 0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7528991d",
   "metadata": {},
   "outputs": [],
   "source": [
    "probabilites = []\n",
    "for h in hours:\n",
    "    p_fail, p_success = lr.predict_proba([[h]])[0]\n",
    "    probabilites.append(p_success)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3042ff20",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x16c7d6980>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(hours, probabilites)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}